Skip to main content
Log in

Throughput Optimization for Interference Aware Underlay CRN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Minimization of interference to primary user (PU), maximizing the throughput and connectivity of secondary users (SUs) while providing the quality of service are the major challenges for success of an underlay mode cognitive radio network (CRN). In this work we propose a model for addressing channel sharing problem for an ad-hoc underlay CRN. The objective is to maximize the throughput of the SUs while keeping the level of interference caused to the PU by the SUs under a given PU threshold. During the underlay communication, each SU receives certain signal strength based on their distances from the PU. A head node is chosen amongst the SUs using a technique based on received signal strength by the SUs. The head collects the information from all the other SUs in terms of data rate requirement, interference produced to PU and the SNR level of the SUs through a common control channel. Using the information received a dynamic programming based model is proposed to decide which SUs can be selected to access a channel for underlay mode communication. A channel is allowed to be accessed such that the throughput of the network is maximized while the overall interference to the PU receiver is kept within the specified tolerable limit. The head node dynamically assigns the channel among the selected SUs. The efficacy of the proposed model has been evaluated with numerical evaluation and simulation study. A greedy algorithm has been formulated which selects the SUs based on their data rate to interference ratio for simulation and comparative study with the proposed model. The results show that the proposed model outperforms the greedy algorithm while increasing the overall connectivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. FCC. (2002). Et Docket no 02-135, spectrum policy task force (sptf) report.

  2. Mitola, J., & Maguire, G. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  3. Bhattacharjee, S., Konar, A., & Bhattacharjee, S. (2011). Throughput maximization problem in a cognitive radio network. International Journal of Machine Learning and Computing, 1(4), 332.

    Article  Google Scholar 

  4. Verma, G., & Sahu, O. P. (2017). Throughput maximization of cognitive radio under the optimization of sensing duration. Wireless Personal Communications. https://doi.org/10.1007/s11277-017-4564-x.

    Google Scholar 

  5. Zhao, Y., Wu, J., & Lu, S. (2011) Throughput maximization in cognitive radio based wireless mesh networks. In IEEE military communications conference (MILCOM) (pp. 260–265).

  6. Xie, S., & Shen, L. (2017). Maximum transmission capacity in cognitive radio networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-017-4348-3.

    Google Scholar 

  7. Li, S., Zheng, Z., Ekici, E., & Shroff, N. (2011). Maximizing system throughput by cooperative sensing in cognitive radio networks. arXiv: 11113041v1 [csNI].

  8. Mazloumi, L., Shahtalebi, K., & Sabahi, M. F. (2015). A simple method for throughput maximization of ofdma based CR networks. Wireless Personal Communications, 85(4), 1869–1882. https://doi.org/10.1007/s11277-015-2875-3.

    Article  Google Scholar 

  9. Tan, L. T., & Le, L. B. (2014) Channel assignment for throughput maximization in cognitive radio networks. arXiv:14041995v1 [csNI].

  10. Benaya, A. M., Rosas, A. A., & Shokair, M. (2017). Proposed scheme for maximization of minimal throughput in mimo underlay cognitive radio networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-017-4456-0.

    Google Scholar 

  11. Moravek, P., Komosny, D., Simek, M., Jelinek, M., Girbau, D., & Lazaro, A. (2010) Signal propagation and distance estimation in wireless sensor networks. In 33rd International conference on telecommunication and signal processing, TSP 2010 (pp. 35–40).

  12. Vu, M., Devroye, N., & Tarokh, V. (2009). On the primary exclusive region of cognitive networks. IEEE Transactions on Wireless Communications, 8(9), 3380–3385.

    Article  Google Scholar 

  13. Haenggi, M., & Ganti, R. K. (2009). Interference in large wireless networks. Foundations and Trends in Networking, NOW, 3(2), 127–248.

    Article  MATH  Google Scholar 

  14. Martello, S., & Toth, P. (1990). Knapsack problems: Algorithms and computer implementations. New York, NY: Wiley.

    MATH  Google Scholar 

  15. Deka, S. K., & Sarma, N. (2017). Opportunity prediction at MAC-layer sensing for ad-hoc cognitive radio networks. Journal of Network and Computer Applications, 82, 140–151.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjib K. Deka.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hussain, A.S., Deka, S.K., Chauhan, P. et al. Throughput Optimization for Interference Aware Underlay CRN. Wireless Pers Commun 107, 325–340 (2019). https://doi.org/10.1007/s11277-019-06257-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06257-6

Keywords

Navigation